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TDAkit (version 0.1.3)

Toolkit for Topological Data Analysis

Description

Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) for a statistical perspective on the topic.

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Version

Install

install.packages('TDAkit')

Monthly Downloads

248

Version

0.1.3

License

MIT + file LICENSE

Maintainer

Kisung You

Last Published

September 21st, 2025

Functions in TDAkit (0.1.3)

gen2holes

Generate Two Intertwined Holes
fskmedoids

K-Medoids Clustering
fsmds

Multidimensional Scaling
plot.homology

Plot Persistent Homology via Barcode or Diagram
plot.landscape

Plot Persistence Landscape
diag2silhouette

Convert Persistence Diagram into Persistent Silhouette
fseqdist

Multi-sample Energy Test of Equal Distributions
diagRips

Compute Vietoris-Rips Complex for Persistent Homology
fshclust

Hierarchical Agglomerative Clustering
diag2landscape

Convert Persistence Diagram into Persistence Landscape
fskgroups

\(k\)-Groups Clustering of Multiple Functional Summaries by Energy Distance
fsdist2

Pairwise \(L_p\) Distance for Two Sets of Functional Summaries
fssc05Z

Spectral Clustering by Zelnik-Manor and Perona (2005)
fsdist

Pairwise \(L_p\) Distance of Multiple Functional Summaries
fsmean

Mean of Multiple Functional Summaries
fsnorm

\(L_p\) Norm of a Single Functional Summary
plkernel

Persistence Landscape Kernel
fssum

Weighted Sum of Multiple Functional Summaries
gen2circles

Generate Two Intersecting Circles
fstsne

t-distributed Stochastic Neighbor Embedding